Description
What happened?
Not quite sure what to actually title this, so feel free to edit it.
I have some netcdf files modeled after the Argo _prof file format (CF Discrete sampling geometry incomplete multidimensional array representation). While working on splitting these into individual profiles, I would occasionally get exceptions thrown complaining about broadcasting. I eventually narrowed this down to some string variables we maintain for historic purposes. Depending on the row split apart, the string data in each cell could be shorter which would result in a stringN having some different N (e.g. string4 = 3 in the CDL). If while serializing, a different string variable is being encoded that actually has length 4, it would reuse the now incorrect string4 dim name.
The above situation seems to only occur when a netCDF file is read back into xarray and the char_dim_name encoding key is set.
What did you expect to happen?
Successful serialization to netCDF.
Minimal Complete Verifiable Example
# setup
import numpy as np
import xarray as xr
one_two = xr.DataArray(np.array(["a", "aa"], dtype="object"), dims=["dim0"])
two_two = xr.DataArray(np.array(["aa", "aa"], dtype="object"), dims=["dim0"])
ds = xr.Dataset({"var0": one_two, "var1": two_two})
ds.var0.encoding["dtype"] = "S1"
ds.var1.encoding["dtype"] = "S1"
# need to write out and read back in
ds.to_netcdf("test.nc")
# only selecting the shorter string will fail
ds1 = xr.load_dataset("test.nc")
ds1[{"dim0": 1}].to_netcdf("ok.nc")
ds1[{"dim0": 0}].to_netcdf("error.nc")
# will work if the char dim name is removed from encoding of the now shorter arr
ds1 = xr.load_dataset("test.nc")
del ds1.var0.encoding["char_dim_name"]
ds1[{"dim0": 0}].to_netcdf("will_work.nc")Relevant log output
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
/var/folders/y1/63dlf4614h5d2cgr5g1t_5lh0000gn/T/ipykernel_64155/447008818.py in <module>
2 ds1 = xr.load_dataset("test.nc")
3 ds1[{"dim0": 1}].to_netcdf("ok.nc")
----> 4 ds1[{"dim0": 0}].to_netcdf("error.nc")
~/.dotfiles/pyenv/versions/3.9.9/envs/jupyter/lib/python3.9/site-packages/xarray/core/dataset.py in to_netcdf(self, path, mode, format, group, engine, encoding, unlimited_dims, compute, invalid_netcdf)
1899 from ..backends.api import to_netcdf
1900
-> 1901 return to_netcdf(
1902 self,
1903 path,
~/.dotfiles/pyenv/versions/3.9.9/envs/jupyter/lib/python3.9/site-packages/xarray/backends/api.py in to_netcdf(dataset, path_or_file, mode, format, group, engine, encoding, unlimited_dims, compute, multifile, invalid_netcdf)
1070 # TODO: allow this work (setting up the file for writing array data)
1071 # to be parallelized with dask
-> 1072 dump_to_store(
1073 dataset, store, writer, encoding=encoding, unlimited_dims=unlimited_dims
1074 )
~/.dotfiles/pyenv/versions/3.9.9/envs/jupyter/lib/python3.9/site-packages/xarray/backends/api.py in dump_to_store(dataset, store, writer, encoder, encoding, unlimited_dims)
1117 variables, attrs = encoder(variables, attrs)
1118
-> 1119 store.store(variables, attrs, check_encoding, writer, unlimited_dims=unlimited_dims)
1120
1121
~/.dotfiles/pyenv/versions/3.9.9/envs/jupyter/lib/python3.9/site-packages/xarray/backends/common.py in store(self, variables, attributes, check_encoding_set, writer, unlimited_dims)
263 self.set_attributes(attributes)
264 self.set_dimensions(variables, unlimited_dims=unlimited_dims)
--> 265 self.set_variables(
266 variables, check_encoding_set, writer, unlimited_dims=unlimited_dims
267 )
~/.dotfiles/pyenv/versions/3.9.9/envs/jupyter/lib/python3.9/site-packages/xarray/backends/common.py in set_variables(self, variables, check_encoding_set, writer, unlimited_dims)
305 )
306
--> 307 writer.add(source, target)
308
309 def set_dimensions(self, variables, unlimited_dims=None):
~/.dotfiles/pyenv/versions/3.9.9/envs/jupyter/lib/python3.9/site-packages/xarray/backends/common.py in add(self, source, target, region)
154 target[region] = source
155 else:
--> 156 target[...] = source
157
158 def sync(self, compute=True):
~/.dotfiles/pyenv/versions/3.9.9/envs/jupyter/lib/python3.9/site-packages/xarray/backends/netCDF4_.py in __setitem__(self, key, value)
70 with self.datastore.lock:
71 data = self.get_array(needs_lock=False)
---> 72 data[key] = value
73 if self.datastore.autoclose:
74 self.datastore.close(needs_lock=False)
src/netCDF4/_netCDF4.pyx in netCDF4._netCDF4.Variable.__setitem__()
src/netCDF4/_netCDF4.pyx in netCDF4._netCDF4.Variable._put()
IndexError: size of data array does not conform to sliceAnything else we need to know?
I've been unable to recreate the specific error I'm getting in a minimal example. However, removing the char_dim_name encoding key does solve this.
When digging in the xarray issues, these looked maybe relevant: #2219 #2895
Actual traceback I get with my data
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/var/folders/y1/63dlf4614h5d2cgr5g1t_5lh0000gn/T/ipykernel_64155/3328648456.py in <module>
----> 1 ds[{"N_PROF": 0}].to_netcdf("test.nc")
~/.dotfiles/pyenv/versions/3.9.9/envs/jupyter/lib/python3.9/site-packages/xarray/core/dataset.py in to_netcdf(self, path, mode, format, group, engine, encoding, unlimited_dims, compute, invalid_netcdf)
1899 from ..backends.api import to_netcdf
1900
-> 1901 return to_netcdf(
1902 self,
1903 path,
~/.dotfiles/pyenv/versions/3.9.9/envs/jupyter/lib/python3.9/site-packages/xarray/backends/api.py in to_netcdf(dataset, path_or_file, mode, format, group, engine, encoding, unlimited_dims, compute, multifile, invalid_netcdf)
1070 # TODO: allow this work (setting up the file for writing array data)
1071 # to be parallelized with dask
-> 1072 dump_to_store(
1073 dataset, store, writer, encoding=encoding, unlimited_dims=unlimited_dims
1074 )
~/.dotfiles/pyenv/versions/3.9.9/envs/jupyter/lib/python3.9/site-packages/xarray/backends/api.py in dump_to_store(dataset, store, writer, encoder, encoding, unlimited_dims)
1117 variables, attrs = encoder(variables, attrs)
1118
-> 1119 store.store(variables, attrs, check_encoding, writer, unlimited_dims=unlimited_dims)
1120
1121
~/.dotfiles/pyenv/versions/3.9.9/envs/jupyter/lib/python3.9/site-packages/xarray/backends/common.py in store(self, variables, attributes, check_encoding_set, writer, unlimited_dims)
263 self.set_attributes(attributes)
264 self.set_dimensions(variables, unlimited_dims=unlimited_dims)
--> 265 self.set_variables(
266 variables, check_encoding_set, writer, unlimited_dims=unlimited_dims
267 )
~/.dotfiles/pyenv/versions/3.9.9/envs/jupyter/lib/python3.9/site-packages/xarray/backends/common.py in set_variables(self, variables, check_encoding_set, writer, unlimited_dims)
305 )
306
--> 307 writer.add(source, target)
308
309 def set_dimensions(self, variables, unlimited_dims=None):
~/.dotfiles/pyenv/versions/3.9.9/envs/jupyter/lib/python3.9/site-packages/xarray/backends/common.py in add(self, source, target, region)
154 target[region] = source
155 else:
--> 156 target[...] = source
157
158 def sync(self, compute=True):
~/.dotfiles/pyenv/versions/3.9.9/envs/jupyter/lib/python3.9/site-packages/xarray/backends/netCDF4_.py in __setitem__(self, key, value)
70 with self.datastore.lock:
71 data = self.get_array(needs_lock=False)
---> 72 data[key] = value
73 if self.datastore.autoclose:
74 self.datastore.close(needs_lock=False)
src/netCDF4/_netCDF4.pyx in netCDF4._netCDF4.Variable.__setitem__()
~/.dotfiles/pyenv/versions/3.9.9/envs/jupyter/lib/python3.9/site-packages/netCDF4/utils.py in _StartCountStride(elem, shape, dimensions, grp, datashape, put, use_get_vars)
354 fullslice = False
355 if fullslice and datashape and put and not hasunlim:
--> 356 datashape = broadcasted_shape(shape, datashape)
357
358 # pad datashape with zeros for dimensions not being sliced (issue #906)
~/.dotfiles/pyenv/versions/3.9.9/envs/jupyter/lib/python3.9/site-packages/netCDF4/utils.py in broadcasted_shape(shp1, shp2)
962 a = as_strided(x, shape=shp1, strides=[0] * len(shp1))
963 b = as_strided(x, shape=shp2, strides=[0] * len(shp2))
--> 964 return np.broadcast(a, b).shape
ValueError: shape mismatch: objects cannot be broadcast to a single shape. Mismatch is between arg 0 with shape (5,) and arg 1 with shape (6,).Environment
INSTALLED VERSIONS
commit: None
python: 3.9.9 (main, Jan 5 2022, 11:21:18)
[Clang 13.0.0 (clang-1300.0.29.30)]
python-bits: 64
OS: Darwin
OS-release: 21.3.0
machine: arm64
processor: arm
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: ('en_US', 'UTF-8')
libhdf5: 1.13.0
libnetcdf: 4.8.1
xarray: 2022.3.0
pandas: 1.3.5
numpy: 1.22.0
scipy: None
netCDF4: 1.5.8
pydap: None
h5netcdf: None
h5py: None
Nio: None
zarr: None
cftime: 1.5.2
nc_time_axis: None
PseudoNetCDF: None
rasterio: None
cfgrib: None
iris: None
bottleneck: None
dask: None
distributed: None
matplotlib: None
cartopy: None
seaborn: None
numbagg: None
fsspec: None
cupy: None
pint: 0.18
sparse: None
setuptools: 58.1.0
pip: 21.2.4
conda: None
pytest: 6.2.5
IPython: 7.31.0
sphinx: 4.4.0